Methods and systems for rapid screening of mild traumatic brain injury

ABSTRACT

The disclosure provides for easy, reliable, and rapid screening of a mild traumatic brain injury (mTBI) based on a modeling of a subject&#39;s tracking of a dynamic target during the course of a simple motor tracking task. The gathered tracking data can be used to calculate tracking errors between the subject&#39;s actual input (e.g., grip force) and the intended target input. The tracking errors may be used to generate numerical values for model parameters that correlate the subject&#39;s responses to the tracking errors during the course of the dynamic motor tracking task. A classification model may be used to compare the model values to multi-subject model values of known diagnoses for mTBI. The entire screening process can be effectively administered in a matter of minutes or less, and with a high degree of accuracy.

FIELD OF THE INVENTION

The present disclosure relates generally to rapid screening for a braininjury, and more specifically to systems and methods for rapid screeningfor mild traumatic brain injury (mTBI) by modeling a subject's responsesduring the course of a dynamic motor tracking task.

BACKGROUND OF THE INVENTION

Mild traumatic brain injury (mTBI), commonly referred to as aconcussion, is a type of traumatic brain injury (TBI) caused by a fall,a blow to the head, or another injury that jars or shakes the braininside the skull. In some instances, there may be no lasting symptoms orill effects because the brain is protected by the skull and cushioned bythe cerebrospinal fluid to absorb impacts. However, the force of animpact may be beyond the ability of the skull and cerebrospinal fluid tofully protect the brain, which can lead to mTBI.

Diagnosing mTBI in its earliest and least problematic stages is criticalto effective intervention, which will improve clinical outcomes andreduce costly long term care. In its Science and Technology (S&T) PathAhead report of Oct. 3, 2012, the Army proclaims “early detection ofTBI” as one of its top challenges, with planned investments of $5million for FY14 and $40 million for FY14-18 to address the challenge.Further, many sporting leagues, from grade school and college athleticsto professional leagues, have a need for thorough implementation ofsideline concussion protocols that easily and reliably screen for mTBIand thus adequately protect athletes from risk of further injury.Therefore, it would be helpful, especially for entities such as themilitary and various sports leagues, to have a mTBI screening systemthat can easily and reliably screen for mTBI immediately following atraumatic exposure.

Common symptoms of mTBI include psychomotor slowing, poor concentration,and decrease in attention retrieval ability, leading to increasedvariability of performance and overall executive dysfunction. Executivedysfunction further causes poor regulation and control of cognitiveprocesses, including working memory, logical reasoning, and problemsolving. Problematically, the onset of mTBI is very subtle, making earlyidentification of mTBI difficult. Indeed, one of the greatest challengesin diagnosing mTBI is that most persons with mTBI do not exhibit clearlydiscernible symptoms immediately following the traumatic exposure.Symptoms may not appear for days, weeks, or even months and when they doappear, they are often nonspecific. Further, persons with mTBI are moresusceptible to additional, and potentially more severe, brain damage.Therefore, the ability to reliably identify mTBI early on in its courseis especially important.

The military in particular has a great need to screen for mTBI among itstroops. Various mTBI symptoms, including headaches, irritability, memoryimpairments, dulled reaction time, and insomnia, lead to decrease inperformance that is particularly dangerous for troops involved in combator in close proximity to a hostile area. Traditional approaches toassessing mTBI, such as detailed neurological evaluations, extensivecognitive testing, and imaging, impose undesirable costs and delays, andare largely impractical to implement on a battlefield. Thus, themilitary has a significant need to make fast and accurate neurocognitiveassessments of its personnel.

Currently available screening procedures for mTBI are largely based on ause of standard questionnaires or self-reporting of the trauma orsymptoms. Standard questionnaires can be administered by emergencypersonnel. Some example questionnaires include the Military AcuteConcussion Evaluation (MACE), the Westmead Post Traumatic Amnesia Scale(PTA), and the Acute Concussion Evaluation (ACE). However, theseevaluation methods are often unreliable and impractical to administerimmediately after injury in the field. For example, MACE is primarilydesigned to be most effective when administered immediately afterinjury, and studies suggest that it is clinically useful within 6 hoursbut ineffective after 12 hours. However, administering a densequestionnaire in the battlefield immediately following a traumaticexposure is not always practical. Recently, a number of other tests havebeen developed to diagnose mTBI, including the AutomatedNeuropsychological Assessment Test (ANAM), the Immediate Post-ConcussionAssessment and Cognitive Testing (ImPACT), and the King-Devick (K-D)Test. Despite these advances, however, screening and diagnosis of mTBIstill largely depend on standard questionnaires and clinicalobservation. There is currently no gold standard or objective means toscreen for and diagnose mTBI.

As disclosed in WO 2014/039861, the content of which is herebyincorporated by reference in its entirety, it is possible to measure andmonitor an individual's motor performance variability immediately afterinjury in the field. More specifically, WO 2014/039861 discloses amethod and system for assessing intra-individual response variability asmanifested in a simple motor task to screen for mTBI and to potentiallydiagnose mTBI or other cognitive impairments.

However, the system disclosed in WO 2014/039861 fails to achieve a highdegree of accuracy for its diagnosis. It implements a simple binaryclassifier and simple metrics that cannot diagnose mTBI with asufficient degree of accuracy to make its use practical as a reliablemTBI screening system. Thus, the system disclosed in WO 2014/039861alone does not negate the need for additional testing of the subject toensure a reliable diagnosis. Therefore, it would be helpful to rapidlyscreen for mTBI with a high degree of accuracy to allow for its easy andreliable use in a real world setting.

SUMMARY OF THE INVENTION

The disclosed systems and methods provide for easy, reliable, and rapidscreening of a mild traumatic brain injury (mTBI) based on a modeling ofa subject's tracking of a dynamic target during the course of a simplemotor tracking task. For example, a subject may use a handheld inputdevice to move a graphical image rendered on a computer display to tracka dynamic visual target on the display. Data is gathered, via thehandheld input device, from a subject performing the motor trackingtask.

In some embodiments, the gathered tracking data can be used to calculatetracking errors between the subject's actual input force, pressure,movement, and/or gesture, and the intended target force, pressure,movement, and/or gesture. The tracking errors may then be used togenerate numerical values for model parameters that best correlate thesubject's responses to the tracking errors during the course of thedynamic motor tracking task. The model values may in turn be compared toother numerical values for corresponding model parameters of knowndiagnoses to screen the subject for mTBI. In some embodiments, the modelvalues of known diagnoses may include data from multiple subjectsincludes both known mTBI patients and non-mTBI controls. Thus, thesubject's model values and the known multi-subject model values can beused by a classification model to diagnose for the presence of mTBI witha high degree of accuracy. The entire screening process can beeffectively administered in a matter of minutes or less.

In some embodiments, a method for screening of a brain injury using oneor more electronic devices includes displaying an image of a dynamictarget on a display for a subject to track using a sensing component andreceiving, from the sensing component, tracking data representing thesubject's tracking of the dynamic target. The method also includesdisplaying in real time an image of a tracker on the displayrepresenting the subject's tracking of the dynamic target, determining amodel value, derived from target data and the tracking data, indicativeof the subject's corrective actions in response to deviations betweenthe tracker and the dynamic target over a period of time, comparing themodel value to one or more multi-subject model values, and outputting ascreening indicator representing the likelihood that the subject has abrain injury based on the compared model values.

In some embodiments, the method further includes comparing the modelvalue to a corresponding prior model value of the subject, andoutputting a prior screening indicator representing a recovery progressof the subject from a brain injury based on the comparison between themodel values. In some embodiments, the prior screening indicatorincludes a screening indicator of the subject from a threshold amount oftime prior to the current time. In some embodiments, the brain injury isa mild traumatic brain injury. In some embodiments, the one or moreelectronic devices include one or more portable devices. In someembodiments, the dynamic target moves in accordance with one or more ofmultiple target modes, and each of the target modes instructs thedynamic target to move in a unique pattern. In some embodiments, thesensing component is a dynamometer that detects the subject's hand gripforce. In some embodiments, the sensing component is an eye-trackingdevice.

In some embodiments, the tracker is an icon on the display that expandsand contracts in response to the subject's input using the sensingcomponent. In some embodiments, the model value is derived from thetracking data via a response model that correlates the deviationsbetween the tracker and the dynamic target to the subject's correctiveactions over a period of time. In some embodiments, the model valueincludes one or more best-fit parameters representing optimized fitvalues quantified by the response model using the tracking data. In someembodiments, the one or more multi-subject model values include one ormore model values of previously tested subjects.

In some embodiments, the previously tested subjects include individualsknown to have a brain injury and individuals known not to have a braininjury. In some embodiments, the previously tested subjects includeindividuals having two or more of gender, height, weight, and age groupin common with the subject. In some embodiments, the previously testedsubjects include individuals employed in the same field of employment asthe subject. In some embodiments, comparing the model value to amulti-subject model value includes implementing a machine learningpredictor that uses a Gaussian process. In some embodiments, the machinelearning predictor includes at least one of a third-party assessmentscore of the subject and third-party assessment scores of themulti-subjects. In some embodiments, the machine learning predictorcompares the subject third-party assessment score to the third-partymulti-subject assessment scores. In some embodiments, the third-partyassessment scores include evaluative scores from publicly availablepost-concussion standard questionnaires In some embodiments, outputtingthe screening indicator includes displaying at least one of a light inone or more colors, an audio signal or text.

In some embodiments, a system for screening of a brain injury includes adisplay screen that displays an image of a dynamic target for a subjectto track using a sensing component and an image of a trackerrepresenting the subject's tracking of the dynamic target and a dataacquisition unit that receives, from the sensing component, trackingdata representing the subject's tracking of the dynamic target. Thesystem also includes a parameter generating unit that determines, basedon target data and the tracking data, a model value indicative of thesubject's corrective actions in response to deviations between thetracker and the dynamic target over a period of time, a memory thatcontains multi-subject model values, and a comparison unit that comparesthe model value to the multi-subject model values to determine alikelihood that the subject has a brain injury.

In some embodiments, a system for screening of a brain injury includes asensing component configured to detect and transmit tracking datarepresenting a subject's tracking of a dynamic target on a display. Thesystem also includes an electronic device in communication with thesensing component, the electronic device including a display, a memory,and a programmable controller to carry out the steps of displaying onthe display an image of a dynamic target for a subject to track usingthe sensing component, receiving, from the sensing component, trackingdata representing the subject's tracking of the dynamic target,displaying on the display, in real time, an image of a tracker on thedisplay representing the subject's tracking of the dynamic target,determining a model value derived from the tracking data and indicativeof the subject's corrective actions in response to deviations betweenthe tracker and the dynamic target over a period of time, comparing themodel value to multi-subject model values, and outputting a screeningindicator representing the likelihood that the subject has a braininjury based on the compared model values.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram illustrating a process for rapid screening of abrain injury by using a dynamic motor tracking task in accordance withsome embodiments.

FIG. 2 is a block diagram illustrating multiple components of a processfor rapid screening of a brain injury by using a dynamic motor trackingtask in accordance with some embodiments.

FIGS. 3A and 3B are graphical depictions of the relative difference inforce between a subject's input force and the intended target force inaccordance with some embodiments.

FIG. 4 illustrates one or more screening systems communicating viawireless communication with a centralized online server in accordancewith some embodiments.

FIG. 5 illustrates one or more screening systems communicating viawireless communication with one or more dispersed online servers inaccordance with some embodiments.

FIG. 6 illustrates an exemplary screening system for measuring andobtaining a subject's tracking data by using a handheld input device anda simple dynamic motor tracking task in accordance with someembodiments.

FIG. 7 illustrates a processing component that is a portable devicebeing used to perform a screening process in accordance with someembodiments.

FIG. 8 illustrates a single portable electronic device that functions asboth a processing component and a sensing component in accordance withsome embodiments.

FIG. 9 is an exemplary display screen showing a graphical image of adynamic target and a graphical image of a subject-controlled tracker inaccordance with some embodiments.

FIG. 10 is an exemplary output screen depicting a diagnosis for mTBI ofa subject following the conclusion of a dynamic motor tracking task inaccordance with some embodiments.

FIG. 11 is a flow diagram illustrating a process for determining aprogress of recovery from a brain injury in accordance with someembodiments.

FIG. 12 is a table comparing classification results from a feedbackresponse model that captures subjects' response to error to conventionalevaluation methods using the same test participants in accordance withsome embodiments.

FIG. 13 is a graphical illustration of several data points representingnumerical values for model parameters of different individuals generatedby an exemplary feedback response model in accordance with someembodiments.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the disclosure and embodiments,reference is made to the accompanying drawings in which it is shown byway of illustration specific embodiments that can be practiced. It is tobe understood that other embodiments and examples can be practiced andchanges can be made without departing from the scope of the disclosure.

In addition, it is also to be understood that the singular forms “a,”“an,” and “the” used in the following description are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It is also to be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It is further to beunderstood that the terms “includes,” “including,” “comprises,” and/orcomprising,” when used herein, specify the presence of stated features,integers, steps, operations, elements, components, and/or units, but donot preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, units, and/or groupsthereof.

There is a need to make easy, reliable, and rapid screening for mTBI dueto the nature and symptoms of the injury. For example, if a servicemanin the battlefield suffers a blow to the head but does not exhibitimmediate signs of injury, the serviceman may remain untested anduntreated for a significant period of time following the traumaticexposure. During this time, the injury may manifest to hinder his/herperformance and put him/her at risk of further, more severe, braindamage from a second accident. Therefore, an ability to easily,reliably, and rapidly detect for mTBI is of great need to entities suchas the military. Such techniques can be administered by any level ofcaregiver in any environment within a matter of minutes to provide arapid and reliable diagnosis, thereby enhancing the likelihood of a fulland speedy recovery down the road. Further, such techniques can be usedto easily monitor an individual's recovery progress over time by acomparison between a present screening result and one or more previousscreening results.

Embodiments of a screening system, processing and sensing components forsuch a system, and associated processes for using such a system aredescribed. The screening system may perform a variety of applications,such as one or more of the following: screening for a brain injury,monitoring recovery progress from a brain injury, screening for drugand/or alcohol intoxication, and gathering individual baseline motorresponse data. Further, the screening system may perform a variety ofapplications, such as one or more of the above listed applications,without any or with only minimal modifications made to its processingand sensing components and its associated processes.

In some embodiments, the screening system is used to screen for a braininjury, in particular mild traumatic brain injury (mTBI), by utilizing asimple motor tracking task. For example, because an onset of mTBI isgenerally accompanied by a sudden decline in motor skills such asreflexes, coordination, and balance, a mTBI-positive individual may beexpected to commit more “errors” during a motor tracking task than anon-mTBI counterpart. Further, the variability in motor tracking abilitymay be more prominent when the intended tracking target is anunpredictably or randomly moving dynamic target. Therefore, thescreening system utilizes a simple dynamic tracking task, preferablyusing an unpredictable or random target, to measure the deviations—i.e.,the tracking errors—between, for example, a subject's actual input forceand the intended target force. These deviations, or tracking errors, maycomprise, for example, delays in response time, overly strong inputforce, overly weak input force, overly fast change in input force,and/or overly slow change in input force.

After gathering a subject's tracking error measurements, the screeningsystem may generate one or more representative numerical valuessummarizing the subject's performance. The representative value—i.e, amodel value—may in turn be compared to representative values ofpre-existing measurements from individuals of known diagnoses. If thepre-existing measurements comprise measurements from both mTBI-positiveand mTBI-negative individuals, measurements from each group may beexpected to cluster together amongst its respective group, therebyforming two largely distinct measurement clusters. The subject'sperformance may then be compared to the pre-existing representativevalues and sorted into the more similar group. If the subject'srepresentative value falls clearly within the cluster of non-mTBIindividuals, the screening system may output an mTBI-negative diagnosisfor the subject. If the subject's representative value falls clearlywithin the cluster of mTBI-positive individuals, the screening systemmay output an mTBI-positive diagnosis for the subject. Otherwise, thescreening system may output a diagnosis with a low confidence value,prompt the subject for a re-screen, and/or request the subject to seekclinical testing. The entire screening process may be administered in amatter of minutes in almost any environment.

While the present disclosure focuses on the use of the disclosedscreening system to screen for mTBI, it should be understood that thescreening system may also be used to test for other types of physicaland/or mental disability or impairment. For example, the screeningsystem may be used for rapid screening of drug and/or alcohol use. Drugand/or alcohol intoxication, even in a small degree, may hinder orinterference with a person's regular motor tracking ability. Therefore,the screening system may be used to perform a screening process, withoutany modifications, to easily, reliably, and rapidly screen drug and/oralcohol intoxication. The screening system may also be used to screenfor mild cognitive impairments, motor disorders such as earlyAlzheimer's, Parkinson's, and Huntington's diseases, post-traumaticstress disorder, and sleep deprivation.

FIGS. 1 and 2 are, respectively, a flow diagram and a block diagramillustrating a screening process in accordance with some embodiments.The screening process may be performed by a screening systemimplementing a dynamic motor tracking task for rapid screening of abrain injury, in particular mild traumatic brain injury (mTBI). Thescreening system comprises a processing component connected to a sensingcomponent. In some embodiments, the processing component and the sensingcomponent may be part of the same device. The processing component mayin turn comprise a display screen and a computing device. In someembodiments, the display screen and the computing device may be part ofthe same device. In some embodiments, the display screen and thecomputing device may be part of separate devices.

The processing component may be a desktop computer, laptop computer,tablet computer, smartphone, smartwatch, and/or any other electronicdevice or a combination thereof capable of performing data collection,data processing, and data analysis. The processing component may includea target generating unit 202A, a data acquisition unit 204B, adecomposition unit 208A, a parameter generating unit 208B, a comparisonunit 210A, and a memory. Each “unit” may include computer software,hardware, combinations thereof, or a component of a computer programcapable of performing a particular function that may be stored on one ormore non-transitory computer readable mediums. Thus, while severaldifferent “units” are mentioned for the sake of clarity, it is to beunderstood that the functions of any combinations of the various units,which may include a target generating unit 202A, a data acquisition unit204B, a decomposition unit 208A, a parameter generating unit 208B, and acomparison unit 210A, may be performed by a single hardware component,for example a microprocessor.

The processing component communicates with a memory that has storedtherein one or more software program, which command one or moremicroprocessors within the processing component to execute the requiredsteps of the disclosed screening process via various functional “units,”described in more detail below. The software programs for executing thedisclosed screening process may be available for download from an onlineapplication store, for example APP STORE for APPLE iOS and GOOGLE PLAYSTORE for ANDROID. In some embodiments, the software program forexecuting the disclosed screening process may be available forinstallation via an optical data storage disc, such as a compact disc(CD), or other portable storage devices such as a Universal Serial Bus(USB) flash drive. In some embodiments, the software program forexecuting the disclosed screening process may be pre-installed in one ormore electronic devices manufactured to function solely as the disclosedprocessing component.

The sensing component may be any input device or component, including aforce-detecting dynamometer or other force sensor, a push-or-pullsensitive device, including a video-game controller or touch screen, aneye-tracking device, a body movement measurement device, and/or anyother device capable of detecting user-applied force, power, and/ortorque and changes in user-applied force, power, and/or torque, or anydevice capable of detecting movements, gestures, and/or signals made bythe user.

In some embodiments, the sensing component may be a grip-forcedynamometer. The grip-force dynamometer detects force applied andchanges in force applied by the grip of a user and transmits thisinformation as tracking data to the processing component. For example,the grip-force dynamometer may be a JAMAR dynamometer or a similardevice. In some embodiments, the sensing component may be apush-sensitive device. The push-sensitive device detects force appliedand changes in force applied by a push, using the hand, arm, and/orfoot, of a user and transmits this information as tracking data to theprocessing component. For example, the push-sensitive device may be abutton that a user can push using the hand, arm, and/or foot. In someembodiments, the sensing component may be an eye-tracking device. Theeye-tracking device detects the position and changes in position of oneor both eyes of a user. For example, the eye-tracking device may be awearable glass with one or more sensors tracking the movements of one orboth eyes. In some embodiments, the sensing component may be a bodymotion measurement device. The body motion measurement device may trackand/or measure bodily movements, gestures, signs and/or signals made bya user. For example, the body motion measurement device may be anaccelerometer-based device capable of measuring bodily activity rangingfrom subtle vibrations to large gestures and movements.

In some embodiments, the screening system may comprise a processingcomponent and a sensing component that are part of a single electronicdevice. Further, the single electronic device may be a portable device.For example, the disclosed screening process may be performed by asmartphone with a gesture or eye-tracking input capability. Thesmartphone is a computing device that may function as the processingcomponent, and the gesture or eye-tracking sensor of the smartphone mayfunction as the sensing component.

At step 102 of FIG. 1 and corresponding components 202 of FIG. 2, thescreening system is carrying out a dynamic motor tracking task. Duringthe motor tracking session, the processing component displays on adisplay screen 202B a graphical image of a dynamic target. A subject,positioned at close proximity to the display screen 202B such that thedynamic target is clearly visible, tracks the dynamic target via asensing component. The movement pattern of the dynamic target on thedisplay screen 202B may be controlled by instructions transmitted from atarget generating unit 202A within the processing component in the formof target data. The target data transmitted from the target generatingunit 202A to generate a particular movement pattern for a dynamictarget—i.e., a particular target mode—may be based on computer softwarepre-programmed into the target generating unit 202A.

Further, the target generating unit 202A may be programmed with aplurality of different target modes, each target mode transmitting itsown unique target data corresponding to a unique target movementpattern. For example, each target mode or a particular target mode maybe programmed to maximize the accuracy of the final diagnosis of thescreening system for individuals of a certain pre-defined group. In someembodiments, each target mode or a particular target mode may betailored to a particular population, community, group, or organization.Differentiating among persons of different groups or organizations, forexample, may compensate for the fact that a professional athletegenerally possesses superior motor skills to a typical non-athlete.Comparing the motor response of a professional athlete to that of atypical non-athlete may not be appropriate because of inherentdifferences in the level of motor skill. Therefore, a screening systemused by the military, for example, may contain a processing componentprogrammed with one or more target modes tailored specifically formilitary personnel. Similarly, a screening system used by a sportingleague or institution, such as the National Football League (NFL) or theNational Collegiate Athletic Association (NCAA)'s football program, maybe programmed with one or more target modes tailored specifically forfootball players.

In some embodiments, each target mode or a particular target mode may betailored to a particular characteristic of a group. A characteristic mayinclude, but is not limited to, gender, age, height, weight, and/orethnicity. Differentiating among persons of different age groups, forexample, may compensate for the fact that a person's motor skillsnaturally decline with age. For instance, there may be separate targetmodes tailored specifically for persons of different age groups, such asages 10-14, ages 15-19, ages 20-39, ages 40-59, and ages 60 and above.Similarly, there may be separate target modes tailored specifically forpersons within a specified height range and/or within a specified weightrange. In some embodiments, each target mode or a particular target modemay be tailored to enhance the level of fun and/or challenge of themotor tracking task. For example, it may be easier to gather trackingdata from children when they are more engaged in successfully completingthe motor tracking task.

In some embodiments, the target data transmitted from the targetgenerating unit 202A may be a sequence of random numbers generated bysoftware code designed to generate a sequence of numbers that lack anypattern. For example, the target generating unit may be programmed witha random number generator having one or more different computationalmethods for generating random data. A particular computational methodfor generating random data may be based on, for instance, a pseudorandomnumber generator (PRNG), a deterministic random bit generator (DRBG), orthe like.

In some embodiments, the computer software for a particular target modemay be representative of an algorithm. The algorithm may generate anoutput, for example a target force, as a function of time. In someembodiments, an algorithm may generate an output, as a function of time,which causes the dynamic target to move in an unpredictable pattern. Anunpredictable pattern represents an irregular, non-periodic, andnon-repetitive movement such that the subject cannot predict in advancethe target's future path. Thus, an unpredictable pattern tests asubject's feedback response; that is, how they respond to, and correctfor, errors when performing the task. In some embodiments, an algorithmmay generate an output, as a function of time, which causes the dynamictarget to move in a predictable pattern. A predictable patternrepresents a regular, periodic, and repetitive movement such that thesubject can predict in advance the target's future path. Thus, apredictable pattern tests a subject's feedforward, or predictive,response; that is, how well a subject can predict where the target willbe at some time in the future.

In some embodiments, an algorithm may use a combination of one or morecontinuous wave functions, including sine waves, square waves, trianglewaves, and/or constants that produce an unpredictable movement pattern.It may also include step functions, wavelets, or other perturbations.For instance, the unpredictable algorithm may be similar in form to thefollowing mathematical equation measuring force:

F(t)=sin (2πf ₁ t)+sin (2πf ₂ t)+sin(2πf ₃ t)+C,

where f₁=0.2335 Hz, f₂=0.37 Hz, f₃=0.45 Hz, and C is a constant thatequals 5% of a subject's maximum grip-force, measured in Newtons,determined at the beginning of the motor tracking task. The frequenciesf₁, f₂, and f₃ should be visually detectable by participants, and withina range that they can feasibly respond to (e.g., 0.1-10 Hz). A targetmode based on this exemplary algorithm may generate an unpredictablemovement pattern as represented graphically in FIG. 3A, which relatesuser-applied force to time. In some embodiments, the algorithm mayrepresent a single periodic sine and/or cosine wave, or a combination ofone or more continuous wave functions, including sine waves, squarewaves, triangle waves, and/or constants that produce a predictablemovement pattern. For example, the algorithm may be one similar in formto the following mathematical equation:

F(t)=2 sin(2πft)+C,

where f=0.30. The frequency f should be visually detectable byparticipants, and within a range that they can feasibly respond to(e.g., 0.1-10 Hz). A target mode based on this exemplary algorithm maygenerate a repetitive movement pattern as represented graphically inFIG. 3B, which relates user-applied force to time.

A motor tracking task based on a particular target mode may beprogrammed to run for a particular period of time. In some embodiments,a target mode may be programmed to generate target data for a timeperiod measured in minutes, for example less than 10 minutes, less than5 minutes, and/or less than 2 minutes. In some embodiments, a targetmode may be programmed to generate target data for a time periodmeasured in seconds, for example less than 45 seconds, less than 30seconds, and/or less than 15 seconds. In some embodiments, an operatoror user of the screening system may be given an option to select aparticular time period from a plurality of choices to accommodate fordiffering environments and circumstances. In some embodiments, anoperator or user of the screening system may be able to enter anycustomized time period before the beginning of the tracking session viaa keyboard input, mouse input, touchscreen input, voice command, or thelike. In some embodiments, the examination time period may be easilyadjustable by the operator before and/or during the tracking session inorder to accommodate for differences in condition among trackingsessions. In some embodiments, some or all available target modes mayinclude an additional “practice phase” period, which may be less than aminute, at the beginning of the tracking session to allow the subject todevelop familiarity with using the sensing component to track thedisplayed dynamic target.

At step 104 of FIG. 1 and corresponding components 204 of FIG. 2, a dataacquisition unit 204B of the processing component may receive from thesensing component, in the form of tracking data, informationrepresenting a subject's motor tracking of a dynamic target 204A duringa motor tracking task. The transmission of the tracking data from thesensing component to the data acquisition unit 204B may occur in realtime. In some embodiments, the tracking data may be transmitted from thesensing component to the data acquisition unit 204B via a cableconnecting the sensing component to the processing component, includingbut not limited to a Universal Serial Bus (USB) connection. In someembodiments, the tracking data may be transmitted from the sensingcomponent to the data acquisition unit 204B via wired internet, forexample a wired local area network (LAN), and/or wireless communication,for example Global System for Mobile Communications (GSM), Enhanced DataGSM Environment (EDGE), Bluetooth, and Wireless Fidelity (Wi-Fi).

The processing component may store the tracking data, retrieved from thedata acquisition unit 204B, and the corresponding target data, retrievedfrom the target generating unit 202A, in a database 210B. For example,the processing component may store the tracking data and thecorresponding target data in a database 210B for future use. Thescreening system may rely on comparing a subject's modeled motortracking result to a plurality of known measurements in making adiagnosis. Therefore, it may be important for the screening system tohave access to an abundance of measured tracking data of a plurality ofindividuals. Accordingly, in some embodiments, the screening system mayautomatically store the subject's measured tracking data, together withits corresponding target data, in a database 210B. In some embodiments,the screening system may ask the operator or subject, via a visual oraudio message, whether it should or should not store the subject'smeasured tracking data and corresponding target data in a database 210B.

Given the importance of gathering and storing an abundance of measuredtracking data from a plurality of individuals, including bothmTBI-positive and mTBI-negative individuals, the screening system may beused simply to collect data. Therefore, in some embodiments, thescreening system may present the operator or subject, via a visual oraudio message, with an option to stop the screening process uponcompletion of the motor tracking task. Further, in some embodiments, thescreening system may be used, without modification, to gather individualbaseline motor response data 210D, which may be incorporated by thecomparison unit 210A at a later step in the screening process.

The database 210B may be stored in one or more memories. For example,the database 210B may be stored in one or more non-volatile memoriescontained within or connected to the processing component of thescreening system. The memory may include but is not limited to a harddisk drive (HDD), a solid-state drive (SSD), a Universal Serial Bus(USB) flash drive, and/or a Secure Digital (SD) memory card.

As illustrated in FIGS. 4 and 5, the database 210B may additionallyand/or alternatively be stored in one or more online servers 420, 520,522 and/or 524. Because the screening system may rely on comparing asubject's modeled motor tracking result to a plurality of knownmeasurements in making a diagnosis, the system may require constantupdates in order to incorporate newly-gathered measurements. As theamount of measured data increases, it may be impractical to constantlyupdate the system with the new data or store the increasing amount ofdata within the processing component itself. If all or a majority of themeasurements were stored and accessible online, however, all screeningsystems connected to the online servers may easily have access to themost up-to-date information and may retrieve them accordingly.

As illustrated in FIG. 4, in some embodiments, system 400 includes oneor more online servers that may be hosted at a centralized location 420and accessible by screening systems 402, 404 and 406 over network 410,such as the internet. For example, online servers may be hosted by asingle institution that gathers and stores tracking data generatedspecifically from the disclosed screening system. As illustrated in FIG.5, in some embodiments, system 500 includes one or more online serversmay be dispersed throughout a plurality of host locations 520, 522and/or 524 and accessible by screening systems 502 over network 510. Forexample, data regarding some users may be stored by a healthcareprovider, data regarding some users may be stored by an institution,organization, or company, and data regarding some users may be stored bya cloud storage service provider.

The processing component may transmit the tracking data and thecorresponding target data to an online server via wired internet, forexample a wired local area network (LAN), and/or wireless communication,for example Wireless Fidelity (WiFi) and mobile telecommunicationstechnology such as Long-Term Evolution (LTE). The transmitted trackingdata and corresponding target data may simply be stored in auser-profile database 210B contained in an online server for potentialfuture use. Additionally, the transmitted tracking data andcorresponding target data may be accessed by a processing componentlocated at a remote location to perform a remote diagnosis.

At step 106 of FIG. 1, the processing component displays at displayscreen 202B of FIG. 2, in real time, a graphical image of a tracker asthe subject performs a motor tracking task. The tracker may be a visualrepresentation of the subject's tracking of the dynamic target via thesensing component. Visually displaying the subject's tracking of thedynamic target in real time enables the subject to continuously observehis/her response to the movement patterns of the dynamic target andreact to his/her errors in tracking the dynamic target. An exemplaryembodiment of a tracker and a dynamic target displayed and interactingon a display screen 202B of the processing component is illustrated inFIG. 9 and further described below. In some embodiments, the graphicalimages of the tracker and dynamic target may be rendered on the samedisplay screen. In some embodiments, the graphical images of the trackerand the dynamic target may be rendered on different sections of the samedisplay screen. In some embodiments, the graphical images of the trackerand the dynamic target may be rendered on different display screens.

At step 108 of FIG. 1 and corresponding components 208 of FIG. 2, themotor tracking task is complete. The gathered tracking data and thecorresponding target data are both transmitted to a parameter generatingunit 208B from the data acquisition unit 204B and from the targetgenerating unit 202A, respectively. In some embodiments, the trackingdata may be transmitted directly from the data acquisition unit 204B tothe parameter generating unit 208B and the corresponding target data maybe transmitted directly from the target generating unit 202A to theparameter generating unit 208B.

In some embodiments, the data acquisition unit 204B may first transmitthe tracking data, and the target generating unit 202A the correspondingtarget data, to a decomposition unit 208A. The decomposition unit 208Amay be, for example, a filtering and decomposition component within theprocessing component that receives signal data and converts the signaldata into a plurality of meaningful data components. The filtering anddecomposition component may receive the tracking data and the targetdata and filter and decompose both data into a plurality of trackingdata components and a plurality of target data components, respectively.For example, the tracking and target data may each be filtered anddecomposed by the decomposition unit 208A into position, velocity, andacceleration, errors in position, velocity, and acceleration(differences from the target), and lag. The various tracking datacomponents and the various target data components may then betransmitted to the parameter generating unit 208B.

Once the tracking data and the corresponding target data, or thetracking data components and the corresponding target data components,have been received, the parameter generating unit 208B parameterizes thesubject's response to errors via a feedback response model on thereceived data or data components to quantify a value for each modelparameter. The model value may consist of a single or multiple numericalvalues representing a single or multiple model parameters. For example,a model value may be a fitted model parameter that optimizes the fit ofthe feedback response model to the subject's tracking data and thetarget data, or to the subject's tracking data components andcorresponding target data components.

In some embodiments, the feedback response model may correlate thesubject's responses to committed tracking error, wherein tracking errorrepresents differences between the tracking data and the target data, ordifferences between the tracking data components and the correspondingtarget data components. For example, if the sensing component is aforce-detection device such as a grip-force dynamometer, the trackingerror may be the difference between the intended target force and theactual subject-applied force. More specifically, the feedback responsemodel may represent a feedback error learning technique that models asubject's feedback response, as opposed to a feedforward response,through a linear parameterization that maps errors in position anderrors in velocity into corrective accelerations.

In some embodiments, the tracking data and the target data may first befiltered or decomposed into a plurality of data components beforereaching the parameter generating unit 208B. For example, a zero-phasedigital filtering using a second-order Butterworth filter with a 2 Hzcutoff frequency may be performed on the data. Following a filtering ordecomposition of the data, the parameter generating unit 208B implementsa feedback response model using the received tracking data componentsand target data components to quantify a plurality of model values. Theplurality of model values may be fitted model parameters that optimizethe fit of the feedback response model to the subject's tracking datacomponents and the target data components.

The feedback response model may correlate the subject's responsiveactions to correct deviations, i.e., the tracking error, between thesubject's actual input, represented by the tracking data and renderedgraphically on a display screen as the tracker, and the subject'sintended target input, represented by the target data and renderedgraphically on a display screen as the dynamic target. In someembodiments, the feedback response model may correlate the subject'sresponsive actions to correct deviations between the actual inputposition and the intended positional target. In some embodiments, thefeedback response model may correlate the subject's responsive actionsto correct deviations between the actual input velocity and the intendedvelocity target. In some embodiments, the feedback response model maycorrelate the subject's responsive actions to correct deviations betweenthe actual input acceleration and the intended acceleration target. Insome embodiments, the feedback response model may correlate thesubject's responsive actions to correct deviations between the actualinput force and the intended force target. In some embodiments, thefeedback response model may correlate the subject's responsive actionsto correct deviations between the actual input pressure and the intendedpressure target. In some embodiments, the feedback response model maycorrelate the subject's responsive actions to correct deviations betweenthe actual input gesture and the intended gesture target. In someembodiments, the feedback response model may correlate the subject'sresponsive actions to correct deviations between the actual input eyemovement and the intended eye movement target. In some embodiments, thefeedback response model may correlate the subject's responsive actionsto correct deviations between the actual input arm movement and theintended arm movement target.

In some embodiments, the feedback response model may transform errors inposition into corrective changes in velocity which reduce the overalltracking error. In some embodiments, the feedback response model maytransform errors in position and/or errors in velocity into correctiveaccelerations which reduce the overall tracking error. Here, positionrefers to any deviation from the target; velocity refers to thederivative of that deviation; acceleration refers to the secondderivative of that deviation. In some embodiments, the feedback responsemodel may further incorporate lag, representing the time difference thatminimizes the subject's actual tracking input and the intended target.

A generated model value may be a numerical value representative of thesubject's performance during the motor tracking task. In other words, asubject's performance may be summarized by one or more numericalvalues—i.e., one or more model values—in order to compare the subject'smotor tracking performance with that of tested subjects. For example, anon-mTBI individual's motor tracking performance may result in arelatively low model value. On the other hand, an mTBI-positiveindividual's motor tracking performance may result in a relatively highmodel value. In some embodiments, a subject's motor tracking performancemay be represented by a plurality of model values, each model valuerepresentative of a particular aspect of the subject's motor trackingperformance. For example, a positional error model value may beindicative of the subject's performance with respect to his/her accuracyregarding the degree of actual applied force in relation to a displayedtarget force. A velocity error model value may be indicative of thesubject's performance with respect to his/her accuracy regarding thedegree of change in actual applied force in relation to a displayedchange in target force.

An exemplary feedback response model, in mathematical form, may berepresented by an algorithm equivalent to or similar to the followingexemplary mathematical equation:

{umlaut over (x)} _((t)) =−K _(p)(x _((t-τ)) −x _(d(t-τ)))−K _(d)({dotover (x)} _((t−τ)) −{dot over (x)} _(d(t-τ))).

This exemplary feedback response model transforms errors in position,x_((t-τ))−x_(d(t-τ)), and errors in velocity, {dot over(x)}_((t-τ))−{dot over (x)}_(d(t-τ)), into corrective accelerations,{umlaut over (x)}_((t-τ)), which reduce the subject's overall trackingerror. x and {dot over (x)} represent the position and velocity,respectively, of the subject's input via the sensing component. x_(d)and {dot over (x)}_(d) represent the position and velocity,respectively, of the target. This exemplary feedback response modelfurther accounts for lag, τ, which is quantified as the time shift thatminimizes the root-mean-square error between the subject's input and thetarget. The errors in position, x_((t-τ))−x_(d(t-τ)), and velocity, {dotover (x)}_((t-τ))−{dot over (x)}_(d(t−τ)), are correlated to thecorrective accelerations, {umlaut over (x)}_((t)), by two fitted modelparameters, K_(p) and K_(d), which, when quantified, are the modelvalues. A zero-phase digital filtering, using for example a second-orderButterworth filter with a 2 Hz cutoff frequency, may be performed on theraw position before differentiating.

In some embodiments, the feedback response model may use a Gauss-Jordanmethod to optimize for a model value that minimizes the mean-squareddifference between the predicted corrective responses and actualcorrective responses of the subject. Prediction accuracies may then bequantified using the correlation between the predicted and actualcorrective responses. For example, the exemplary feedback response modelmay use a Gauss-Jordan method to optimize for values of K_(p) and K_(d)that minimize the mean-squared difference between the predictedcorrective accelerations and actual corrective accelerations of thesubject. Prediction accuracies may then be quantified using thecorrelation between the predicted and actual corrective accelerations.

At step 110 of FIG. 1 and corresponding components 210 of FIG. 2, themodel value or the plurality of model values generated by the parametergenerating unit 208B are transmitted to a comparison unit 210A, whichcompares the model value to one or more corresponding multi-subjectmodel values in order to determine the subject's diagnosis.

As described previously, a model value represents a representativenumerical value summarizing the subject's performance from the motortracking task. A subject's generated model value may in turn be comparedto model values from pre-existing measurements of known individuals. Ifthe pre-existing measurements comprise measurements from bothmTBI-positive and mTBI-negative individuals, measurements from eachgroup may be expected to cluster together amongst its respective group,thereby forming two largely distinct measurement clusters. The subject'sperformance may then be compared to the pre-measured performances andsorted into the more similar group. If the subject's model value fallsclearly within the cluster of mTBI-negative individuals, the screeningsystem may output an mTBI-negative diagnosis for the subject. If thesubject's representative value falls clearly within the cluster ofmTBI-positive individuals, the screening system may output anmTBI-positive diagnosis for the subject. Otherwise, the screening systemmay output a diagnosis with a low confidence value, prompt the subjectfor a re-screen, and/or request the subject to seek clinical testing.

In some embodiments, the comparison unit 210A may implement a machinelearning predictor, which may be trained on previously standardizedmulti-subject model values, on the subject's model value and thecorresponding multi-subject model values to determine a diagnosis forthe subject. In some embodiments, the machine learning predictor may bea statistical classifier, for example a Gaussian Process classifier,which is trained on previously standardized model values. Thestatistical classifier may use a constant mean function and a squaredexponential covariance function with an Automatic RelevanceDetermination (ARD) distance measure. The statistical classifier may beadapted for binary classification with a cumulative Gaussian likelihoodfunction and inference by Expectation Propagation. The classificationanalysis may be performed by selecting the most likely class among mTBIand non-mTBI control groups. Further, the classification analysis may beperformed by using the Gaussian Processes for Machine Learning (GPML)library for MATLAB.

For example, a parameter generating unit 208B implementing the exemplaryfeedback response model may generate and transmit model values K_(p) andK_(d) to the comparison unit 210A. After obtaining the model valuesK_(p) and K_(d) , the comparison unit 210A, which may be a machinelearning predictor, may implement a Gaussian Process classifier that istrained on previously standardized multi-subject model values for K_(p),K_(d), and τ. The classifier may use a constant mean function, and asquare exponential covariance function with an Automated RelevanceDetermination (ARD) distance measure. The length scales for K_(p) and τmay initially be set to 1, and the length scale for K_(d) may be set to5. K_(p) and K_(d) may range from 0.1 to 100. The Gaussian Processclassifier may be adapted for binary classification with a cumulativeGaussian likelihood function and inference by Expectation Propagation.The classification analysis may be performed by selecting the mostlikely class among mTBI and mTBI-negative control groups. Further, theclassification analysis may be performed by using the Gaussian Processesfor Machine Learning (GPML) library for MATLAB.

Multi-subject model values may be model values of prior screeningsubjects, which may or may not include the current subject, stored in adatabase 210B. The prior screening subjects may comprise both non-mTBIcontrols and mTBI patients. In some embodiments, the database 210B maybe stored in a non-volatile memory contained within or connected to theprocessing component. The memory may include but is not limited to ahard disk drive (HDD), a solid-state drive (SSD), a Universal Serial Bus(USB) flash drive, and/or a Secure Digital (SD) memory card. In someembodiments, database 210B may be stored in one or more online servershosted at a centralized location. The processing component may retrievethe multi-subject model values when needed from the online server viawired internet, for example a wired local area network (LAN), and/orwireless communication, for example Wireless Fidelity (WiFi) and mobiletelecommunications technology such as Long-Term Evolution (LTE).

At step 112 of FIG. 1 and corresponding components 212 of FIG. 2, theprocessing component may output a screening indicator 212Arepresentative of the diagnosis made by the comparison unit 210A. Insome embodiments, the screening indicator 212A may display the diagnosison a display screen as YES or NO, POSITIVE or NEGATIVE, or the like. Insome embodiments, the screening indicator 212A may be accompanied by astatistical confidence interval 212B, for example a likelihood that thediagnosis is correct or incorrect. In some embodiments, the screeningindicator 212A may be a recovery indicator or may be accompanied by arecovery indicator, wherein the recovery indicator is indicative of therecovery progress of the subject.

FIG. 6 is a perspective view illustrating a subject 609 performing adynamic motor tracking task with a screening system 600, comprising aprocessing component 601 and a sensing component 602, in accordance withsome embodiments. In some embodiments, the sensing component may be agrip-force dynamometer 602 as illustrated in FIG. 6. The grip-forcedynamometer 602 detects changes in force applied by a hand grip of asubject and transmits this information, in real time, as tracking datato the data acquisition unit.

The subject 609, gripping the dynamometer 602 in one hand, watches avisual image of a dynamic target 605 moving in a pattern across adisplay screen 604 as instructed by the target generating unit (notshown). In some embodiments, the dynamometer 602 may transmit trackingdata to the processing component 601 via a cable 603. In someembodiments, the dynamometer 602 may transmit tracking data to theprocessing component 601 via wired internet, for example a wired localarea network (LAN), and/or wireless communication, for example GlobalSystem for Mobile Communications (GSM), Enhanced Data GSM Environment(EDGE), Bluetooth, and Wireless Fidelity (Wi-Fi).

The subject 609 adjusts his/her grip-force on the dynamometer 602 totrack the image of the dynamic target 605 on the display screen 604. Toaccomplish this, the dynamometer 602 may contain a transducer thatdetects changes in the subject's grip-force and conveys thisinformation, in real time, as tracking data to the data acquisition unit(not shown). The data acquisition unit then instructs the display screen604 to display, in real time, an image of the subject-controlled tracker606. In some embodiments, the tracker 606 may be a movable, changeable,or adjustable graphical image on the same display screen as the target605. In some embodiments, the tracker 606 may be a movable, changeable,or adjustable graphical image on a different potion of the same displayscreen or on a different display screen from the target 605.

FIG. 7 is a perspective view illustrating a subject 709 performing adynamic motor tracking task with a screening system 700, comprising aprocessing component 701 and a sensing component 702, wherein theprocessing component 701 is a portable device, in accordance with someembodiments. The processing component may consist of one or moreportable electronic devices with a display screen. The portableelectronic device may be a laptop computer, tablet computer, smartphone,and/or smartwatch that may include a target generating unit, a dataacquisition unit, a decomposition unit, a parameter generating unit, acomparison unit, and a memory. While several different “units” arementioned for the sake of clarity, it is to be understood that thefunctions of any combinations of the various units may be performed by asingle hardware component within the one or more portable electronicdevices, for example a microprocessor.

The portable processing component 701 may communicate with a sensingcomponent 702 via a cable 703 or wireless communication to receivetracking data from the sensing component. In some embodiments, thetracking data may be transmitted from the portable sensing component tothe processing component via a cable 703. In some embodiments, thetracking data may be transmitted from the sensing component to theportable processing component via wired internet, for example a wiredlocal area network (LAN), and/or wireless communication, for exampleGlobal System for Mobile Communications (GSM), Enhanced Data GSMEnvironment (EDGE), Bluetooth, and Wireless Fidelity (Wi-Fi).

FIG. 8 illustrates a subject 809 undergoing an examination using thedisclosed screening systems and processes using a single smartphone 801acting as both a portable processing component and sensing component. Insome embodiments, the screening system 800, comprising a processingcomponent and a sensing component, may be part of one electronic device.In the illustrated embodiment, for example, a smartphone 801 may beinstalled with a software program for executing the disclosed screeningprocess, and the sensing component may be a gesture or eye-trackingcomponent 802 of the smartphone 801. Thus, the subject 809 may undergo adynamic motor tracking test by tracking a graphical image of the target805 on the smartphone's display screen 804 using gestures or his/her eyemovements. A graphical image of the tracker 806, representing thesubject's tracking of the dynamic target 805 using his/her eyemovements, is also depicted on the display screen 804 in real time.

FIG. 9 is a display screen 904 showing a graphical image of the targetand a graphical image of the tracker during the course of a dynamicmotor tracking task in accordance with some embodiments. In theillustrated embodiment, the target is a horizontal band 905 that movesup and down the display screen in an unpredictable pattern as instructedby the target generating unit. An unpredictable pattern represents anirregular, non-periodic, and non-repetitive movement such that thesubject cannot predict in advance the target's future path. Thus, anunpredictable pattern tests a subject's feedback response. On the otherhand, a predictable pattern, which represents a regular, periodic, andrepetitive movement such that the subject can predict in advance thetarget's future path, tests a subject's feedforward response.

The target generating unit may implement, for example, an algorithmcomprising a combination of one or more sine waves, cosine waves, and/orconstants that produce an unpredictable movement pattern. For instance,the unpredictable algorithm may be similar in form to the followingmathematical equation measuring force:

F(t)=sin (2πf ₁ t)+sin(2πf ₂ t)+sin(2πf ₃ t)+C,

where f₁=0.2335 Hz, f₂=0.37 Hz, f₃=0.45 Hz, and C is a constant thatequals 5% of a subject's maximum grip-force, measured in Newtons,determined at the beginning of the motor tracking task. The frequenciesf₁, f₂, and f₃ should be visually detectable by participants, and withina range that they can feasibly respond to (e.g., 0.1-10 Hz). A targetmode based on this exemplary algorithm may generate an unpredictablemovement pattern as represented graphically in FIG. 3A, which relatesuser-applied force to time. The target generating unit may alsoimplement, for example, an algorithm comprising a single periodic sineand/or cosine wave, or a combination of one or more sine waves, cosinewaves, and/or constants that produce a predictable movement pattern. Forexample, the algorithm may be one similar in form to the followingmathematical equation:

F(t)=2 sin(2πft)+C,

where f=0.30. The frequency f should be visually detectable byparticipants, and within a range that they can feasibly respond to(e.g., 0.1-10 Hz). A target mode based on this exemplary algorithm maygenerate a repetitive movement pattern as represented graphically inFIG. 3B, which relates user-applied force to time.

In the illustrated embodiment, the target is a horizontal band 905 andthe subject-controlled tracker is a vertical band 906 located beneaththe horizontal band 905. An operator, or a textual or audio messageplayed by the processing component, may instruct the subject to “track”the dynamic target by controlling the vertical band. In the illustratedembodiment, “tracking” may comprise the subject attempting to keep thetip 906A of the vertical band 906 aligned with the bottom edge 905A ofthe horizontal band 905 over the course of the dynamic motor trackingtask. For example, an increase in applied grip-force on the dynamometermay increase the height of the vertical band, and a decrease in appliedgrip-force may decrease the height of the vertical band. Accordingly,the subject may track the target by controlling the height of thevertical band using the dynamometer to heighten or shrink the verticalband in response to the up-and-down movements of the horizontal band.The change in height of the vertical band in response to changes in thesubject's applied grip-force is shown on the display screen in realtime; thus, the subject can constantly visualize his/her trackingactions and continuously attempt to make corrections when the bottomedge of the horizontal band moves out of line with the tip of thevertical band.

In some embodiments, the vertical band may instead move up and down inresponse to the subject's control. An increase in applied grip-force onthe dynamometer may move the vertical band vertically upwards, and adecrease in applied grip-force may move the vertical band verticallydownwards. Accordingly, the subject may attempt to keep the tip of thevertical band aligned with the bottom edge of the horizontal band bycontrolling the vertical position of the vertical band using thedynamometer.

It should be noted that the dynamic motor tracking task illustrated inFIG. 9 is only exemplary. The dynamic motor tracking task is not limitedto the use of two moving or adjustable bands. In some embodiments, thetarget may be a differently-shaped dynamic graphical image, such as acircle or a square, which moves in one or more directions throughout thedisplay screen in an unpredictable pattern as instructed by the targetgenerating unit. In accord, the tracker may also be a differently-shapedgraphical image that tracks the target via changes in size or position.In some embodiments, the target may be a dynamic line or point on agraph that moves in one or more directions throughout the display screenin an unpredictable pattern as instructed by the target generating unit.In accord, the tracker may also be a line or point on the graph. In someembodiments, the target may be a shape, for example a circle, triangle,rectangle, box, ball, or cylinder, which dynamically changes in size orvolume. In accord, the tracker may also be a circle, triangle,rectangle, box, ball, cylinder, or the like, that tracks changes in sizeor volume of the target by expanding and contracting in response tochanges in the subject's input.

The target generating unit may be capable of generating a plurality ofdifferent target modes. In some embodiments, the plurality of differenttarget modes may appear on a menu screen or options screen such that itis easily selectable by an operator or user. In some embodiments, thetarget modes may be selectable by a physical button on the processingcomponent. Each target mode may be programmed to generate instructionsbased on an algorithm most likely to maximize the accuracy of the finaldiagnosis depending on one or more characteristics of a potentialsubject. More specifically, each target mode may represent a uniquealgorithm tailored to a particular segment of a population, community,or entity. For example, a processing component used by the military maybe programmed with one or more target modes representing uniquealgorithms tailored to military personnel. Similarly, a processingcomponent used by a sports league, for instance the National FootballLeague (NFL), may be programmed with one or more target modesrepresenting unique algorithms tailored to professional footballplayers. Further, each target mode may represent a unique algorithmtailored to a particular characteristic of a group. The characteristicmay include, but is not limited to, gender, height, weight, and agegroup. For example, there may be separate target modes tailored to menof a certain age group or women of a certain age group.

FIG. 10 is an output screen 100 indicating a diagnosis following theconclusion of a test in accordance with some embodiments. The displayscreen 101 depicts a screening indicator 103 that is indicative of thediagnosis. The diagnosis may be displayed in simple words, for exampleYES or NO, POSITIVE or NEGATIVE, or the like. In some embodiments, thediagnosis may additionally or alternatively be outputted via one or morelight signals. For example, the screening indicator may show a red colorfor a positive diagnosis and a green color for a negative diagnosis. Insome embodiments, the diagnosis may additionally or alternatively beoutputted via audio. For example, the screening system may furtherinclude a speaker, which may play an audio message indicating thediagnosis.

The comparison unit may also generate a confidence interval whichreflects the tracking data's support for the diagnosed classification.In such case, the output screen 100 may further include a statisticalconfidence value 104 based on the generated confidence interval. In someembodiments, the statistical confidence value may indicate thelikelihood that the diagnosis is correct. In some embodiments, thestatistical confidence value may indicate the likelihood that thesubject has or does not have mTBI, irrespective of the diagnosis. Insome embodiments, the statistical confidence value 104 may be aninterval ranging between two percentages.

In some embodiments, the comparison unit may further apply theconfidence interval to reduce false negative diagnoses. For example, thecomparison unit may be biased to produce negative classifications onlywhen the particular negative classification is highly confident based onthe confidence interval, thereby reducing the likelihood of generatingfalse negatives.

In some embodiments, if the confidence value is below a certainthreshold, for example below 70%, the comparison unit may cause thescreening system to output a request to re-take the motor trackingexamination. In some embodiments, if the confidence value is below acertain threshold, for example below 70%, and the subject has alreadybeen tested at least a pre-determined number of times, for example atleast three times, the comparison unit may cause the screening system tooutput a request to seek a clinical diagnosis.

In addition to tracking data gathered via the dynamic motor trackingtask, the comparison unit may further incorporate other subject-specificdata that may be available from the subject's previous screening testsor conventional evaluative measurement scores.

In some embodiments, the comparison unit may incorporate baseline motorresponse data of the subject as an additional barometer to compare tothe subject's current performance. Further, the screening system may beused, without modification, to gather individual baseline motor responsedata. Baseline motor response data may be representative tracking dataof a subject obtained using the screening system at a time when thesubject has not been exposed to a traumatic incident. Differentindividuals may, even when fully healthy, vary wildly in terms ofinherent motor tracking ability. For example, trained athletes maypossess stronger inherent motor skills compared to a typicalnon-athlete. Thus, it may be the case that a trained athlete may performbetter on a motor tracking task, even if concussed, than a non-athletemay perform on the same motor tracking task, even when not concussed.Therefore, it may improve the reliability of the screen system if thecomparison system incorporates baseline motor response data in order toaccount for inherent differences in motor skills among individuals.

In some embodiments, the comparison unit may incorporatesubject-specific conventional mTBI evaluative scores and measurements.For example, the processing component may incorporate additionalrelevant measurements from conventional mTBI evaluation sources inaddition to the tracking data to determine a diagnosis. This may includetest scores from standard mTBI questionnaires, including but not limitedto the Military Acute Concussion Evaluation (MACE), the Westmead PostTraumatic Amnesia Scale (PTA), and the Acute Concussion Evaluation(ACE). If conventional measurements are incorporated into the screeninganalysis, the output screen may depict two different screeningindicators, one incorporating the subject's conventional data and theother ignoring the subject's conventional data.

In order to retrieve subject-specific third-party evaluation scores, thescreening system may have access to a database containing a collectionof user profiles of known subjects from where it can retrieve thesubject's evaluation history. In some embodiments, the user-profiledatabase may be contained in one or more non-volatile memories thatcomprise a part of the processing component, for example a hard diskdrive (HDD) or a solid-state drive (SSD). In some embodiments, theuser-profile database may be contained in a portable data storagedevice, for example a Universal Serial Bus (USB) flash drive or a SecureDigital (SD) memory card. In some embodiments, the user-profile databasemay be contained in one or more online servers, for example servershosted at a centralized location. The processing component may thenaccess the user-profile database stored in one or more online serversvia wired internet, for example a wired local area network (LAN), and/orwireless communication, for example Wireless Fidelity (WiFi) and mobiletelecommunications technology such as Long-Term Evolution (LTE).

The output screen 100 may also depict on the display screen 101 a plot102 demonstrating the performance of the subject in graphical form. Forexample, the plot may convey the result of a completed grip-forcedynamometry task, based on an unpredictable pattern mode, in terms offorce (N) as a function of time (seconds). A solid line may representthe movement of the target in terms of force over time, and a dottedline may represent the movement of the tracker in terms of force overtime. The plot may be generated from the tracking data received from thesensing component, and the target data received from the targetgenerating unit.

FIG. 11 is a flow diagram illustrating a recovery screening process inaccordance with some embodiments. At step 1102, the recovery screeningprocess is performed using the disclosed screening system and process,without any modifications, by a subject who has already been diagnosedusing the screening system at least one previous time prior to thecurrent examination. Accordingly, there may be one or more prior modelvalues from previous tests belonging to the particular subject stored ina database.

At step 1104, a model value generated from a present screening processis compared to the prior one or more corresponding model valuesbelonging to the same subject retrieved from the database. In someembodiments, a change in numerical value of the same model parameterover two or more tests at different points in time may be indicative ofa progressing recovery from mTBI. For example, the numerical value of amodel parameter may decrease over subsequent tests. If the decrease isexponential, the decrease may be deemed significant.

In some embodiments, a particular model parameter may be particularlysensitive to recovery relative to the other model parameters. Forexample, because the numerical values for the model parameters K_(p),K_(d), and τ generated by a parameter generating unit implementing theexemplary feedback response model described above with respect to FIG. 1are sensitive to mTBI, their numerical values for the same subject maychange as the condition of the subject's injury improves ordeteriorates. In particular, the differential parameter K_(d) maydecrease exponentially over time since the time of injury as the subjectrecovers from mTBI. A substantial decrease in the numerical value of amodel parameter over time, such as an exponential decrease, may beindicative of progressing recovery from mTBI. On the other hand, asubstantial increase in the numerical value of a model parameter overtime, such as an exponential increase, may be indicative of worsening ofmTBI.

At step 1106, the processing component may output, in addition to oralternatively to outputting the screening indicator, a recoveryindicator representing the recovery progress of the subject based on thecomparison between one or more present and prior corresponding modelvalues. In some embodiments, the processing component may further outputinformation regarding which particular model parameter was most relevantto the recovery diagnosis. For a screening process implementing theexemplary feedback response model with model parameters K_(p) , K_(d),and τ, the differential parameter K_(d) may be most relevant based onits exponential decrease over time. In some embodiments, the processingcomponent may further output an estimated recovery time to fullrecovery. For example, the comparison unit may determine that a certainamount of decrease in one or more of the subject's model values willresult in a re-classification of the subject from mTBI-positive tomTBI-negative. The comparison unit may then estimate an approximate timethat will need to pass before the relevant model value(s) crosses thisthreshold based on the subject's prior testing history and recoveryprogress results.

FIG. 12 is a table comparing classification results from the exemplaryfeedback response model, discussed above with respect to FIG. 1, toother publicly available evaluation methods with the same twenty-ninetest participants. The twenty-nine test participants comprise fourteennon-mTBI controls and fifteen mTBI patients. The exemplary feedbackresponse model, using K_(p), K_(d) and τ, is tested to have a predictiveaccuracy of 89.7% using a leave-one-out cross-validation method overtest participants. By contrast, both the MACE test and the Physicians'Health Questionnaire (PHQ-9) shows a predictive accuracy of 72.4% basedon the same twenty-nine test participants. Classification via TrailsMaking Test A is significantly less accurate (p=0.024), at 55.2% basedon same twenty-nine test participants. Simpler metrics such as thestandard deviation of the tracking error (STDEV) and τ (lag) is alsoless accurate (p=0.041), at 69.0% based on the same twenty-nine testparticipants.

FIG. 13 is a three dimensional graph illustrating the comparison unit,for example a machine learning predictor, implementing a GaussianProcess classifier to three model parameters K_(p), K_(d) and τgenerated from the exemplary feedback response model discussed withrespect to FIG. 1 in accordance with some embodiments. The responsegraph depicts a cluster of circle data points representing mTBI patientsand a cluster of square data points representing non-mTBI controls. Afeedback response model and machine learning predictor that generate aresponse graph depicting data points that are more tightly-clustered mayrepresent a screening process with a higher degree of accuracy. Further,a data point of a particular response graph that is further displacedfrom the center point of a data cluster may represent a data point witha diagnosis having a lower statistical confidence value. Analogously, adata point of a particular response graph that is closely placed to thecenter point of a data cluster may represent a data point with adiagnosis having a higher statistical confidence value.

In some embodiments, the comparison unit may combine the model valuesquantified from the dynamic motor tracking task with other behavioraltests from third-party evaluative sources. A combination of thedisclosed dynamic motor tracking task with other available tests mayfurther enhance the accuracy of the diagnosis. Possible complimentarybehavioral tests include but are not limited to the AutomatedNeuropsychological Assessment Test (ANAM), the Immediate Post-ConcussionAssessment and Cognitive Testing (ImPACT), and the King-Devick (K-D)Test.

1. A method for screening of a brain injury using one or more electronicdevices comprising: displaying an image of a dynamic target on a displayfor a subject to track using a sensing component; receiving, from thesensing component, tracking data representing the subject's tracking ofthe dynamic target; displaying in real time an image of a tracker on thedisplay representing the subject's tracking of the dynamic target;determining a model value, derived from target data and the trackingdata, indicative of the subject's corrective actions in response todeviations between the tracker and the dynamic target over a period oftime; comparing the model value to one or more multi-subject modelvalues; and outputting a screening indicator representing the likelihoodthat the subject has a brain injury based on the compared model values.2. The method of claim 1, further comprising: comparing the model valueto a corresponding prior model value of the subject; and outputting aprior screening indicator representing a recovery progress of thesubject from a brain injury based on the comparison between the modelvalues.
 3. The method of claim 2, wherein the prior screening indicatorcomprises a screening indicator of the subject from a threshold amountof time prior to the current time.
 4. The method of claim 1, wherein thebrain injury is a mild traumatic brain injury.
 5. The method of claim 1,wherein the one or more electronic devices comprise one or more portabledevices.
 6. The method of claim 1, wherein the dynamic target moves inaccordance with one or more of a plurality of target modes, and each ofthe target modes instructs the dynamic target to move in a uniquepattern.
 7. The method of claim 1, wherein the sensing component is adynamometer that detects the subject's hand grip force.
 8. The method ofclaim 1, wherein the sensing component is an eye-tracking device.
 9. Themethod of claim 1, wherein the tracker is an icon on the display thatexpands and contracts in response to the subject's input using thesensing component.
 10. The method of claim 1, wherein the model value isderived from the tracking data via a response model that correlates thedeviations between the tracker and the dynamic target to the subject'scorrective actions over a period of time.
 11. The method of claim 10,wherein the model value comprises one or more best-fit parametersrepresenting optimized fit values quantified by the response model usingthe tracking data.
 12. The method of claim 1, wherein the one or moremulti-subject model values comprise one or more model values ofpreviously tested subjects.
 13. The method of claim 12, wherein thepreviously tested subjects comprise individuals known to have a braininjury and individuals known not to have a brain injury.
 14. The methodof claim 13, wherein the previously tested subjects comprise individualshaving two or more of gender, height, weight, and age group in commonwith the subject.
 15. The method of claim 14, wherein the previouslytested subjects comprise individuals employed in the same field ofemployment as the subject.
 16. The method of claim 1, wherein comparingthe model value to the one or more multi-subject model values comprisesimplementing a machine learning predictor that uses a Gaussian process.17. The method of claim 16, comprising comparing a plurality ofassessment scores of the subject to a plurality of assessment scores ofmultiple subjects from the multi-subject model.
 18. The method of claim17, wherein the plurality of assessment scores of the multiple subjectscomprise evaluative scores from post-concussion questionnaires.
 19. Themethod of claim 1, wherein outputting the screening indicator comprisesdisplaying at least one of a light in one or more colors, an audiosignal or text.
 20. A system for screening of a brain injury, the systemcomprising: a display screen that displays an image of a dynamic targetfor a subject to track using a sensing component and an image of atracker representing the subject's tracking of the dynamic target; adata acquisition unit that receives, from the sensing component,tracking data representing the subject's tracking of the dynamic target;a parameter generating unit that determines, based on target data andthe tracking data, a model value indicative of the subject's correctiveactions in response to deviations between the tracker and the dynamictarget over a period of time; a memory that contains multi-subject modelvalues; and a comparison unit that compares the model value to themulti-subject model values to determine a likelihood that the subjecthas a brain injury.
 21. A system for screening of a brain injurycomprising: a sensing component configured to detect and transmittracking data representing a subject's tracking of a dynamic target on adisplay; an electronic device in communication with the sensingcomponent, the electronic device including a display, a memory, and aprogrammable controller to carry out the steps of: displaying on thedisplay an image of a dynamic target for a subject to track using thesensing component; receiving, from the sensing component, tracking datarepresenting the subject's tracking of the dynamic target; displaying onthe display, in real time, an image of a tracker on the displayrepresenting the subject's tracking of the dynamic target; determining amodel value derived from the tracking data and indicative of thesubject's corrective actions in response to deviations between thetracker and the dynamic target over a period of time; comparing themodel value to multi-subject model values; and outputting a screeningindicator representing the likelihood that the subject has a braininjury based on the compared model values.